Optical Character Recognition (OCR) is a critical research direction in computer vision. To address the inefficiencies and high error rates of manual inkjet code recognition, this study develops an end-to-end deep learning-based inkjet code recognition solution, aiming to improve accuracy while reducing model size for mobile applicability. First, the study summarizes domestic and international OCR research, analyzes mainstream algorithms, and introduces fundamental neural networks (CNN, RNN), including the structure of CNN layers and improvements to RNN limitations. Subsequently, targeting the shortcomings of large size and slow computation in traditional feature extraction networks, the study optimizes the CRNN+CTC model by proposing a lightweight OCR algorithm based on depthwise separable convolution, replacing conventional feature extractors with MobileNetV3 to reduce model size and enhance performance. Experimental results show that compared with the original CRNN model and three mainstream feature extraction algorithms, the proposed lightweight algorithm outperforms in average recognition time, accuracy, and model size. Tests on real inkjet datasets further confirm its robustness and high accuracy in recognizing blurred, deformed, or distorted characters.